Software Engineering AI Trainer (Ecuador)

📋 Description

• • Design and implement multi-file software engineering tasks focused on bug resolution, feature implementation, refactoring, and test generation in production-like environments
• • Write clear, detailed natural-language specifications that accurately define coding requirements and expected outcomes for AI training datasets
• • Develop reference implementations in Python, JavaScript/TypeScript, or other supported languages that serve as correct solutions for generated tasks
• • Create and extend comprehensive unit and integration test suites using frameworks such as pytest, Jest, JUnit, xUnit, or Go testing to validate task solutions
• • Review peer-generated coding tasks and submissions for correctness, clarity, realism, and alignment with specifications
• • Identify edge cases, ambiguities, logical inconsistencies, and potential failure modes in both task designs and peer responses
• • Ensure strict alignment between natural-language specifications, reference code implementations, and expected behavioral outputs
• • Navigate and analyze large, complex, multi-file codebases to assess code quality, architecture, and maintainability
• • Participate in structured code reviews to evaluate peer submissions for adherence to best practices, readability, and robustness
• • Contribute to production-scale training datasets used by a leading AI lab to improve model performance in real-world software engineering scenarios
• • Maintain high standards of precision in task creation, ensuring all generated content reflects authentic, industry-standard software development challenges
• • Manage own schedule and workflow as a project-based consultant with autonomy over task selection and delivery timelines
• • Work remotely with no fixed hours; deliverables are assessed on project completion, not time spent
• • Use own tools and development environments; no company-provided hardware or software is provided
• • May simultaneously engage with other vendors or employers, provided such arrangements do not conflict with project confidentiality or scope
• • Collaborate asynchronously with global teams to refine task quality, resolve ambiguities, and improve dataset consistency
• • Deliver structured, actionable feedback on peer-generated content, highlighting strengths, weaknesses, and areas for improvement
• • Produce validation artifacts including test results, output logs, and correctness annotations to support AI training evaluation
• • Ensure all submitted work is original, well-documented, and free of plagiarism or unauthorized reuse of third-party code
• • Adapt quickly to evolving task requirements and feedback cycles from the AI lab
• • Maintain consistent quality across diverse coding domains, including backend systems, API design, data structures, and system integration
• • Prioritize realism in task design to mirror actual software development workflows encountered in professional environments
• • Document implementation decisions and rationale to support reproducibility and auditability of training data
• • Respond to iterative feedback on task submissions to refine accuracy, scope, and instructional clarity
• • Adhere to confidentiality agreements and data handling protocols for proprietary AI training materials
• • Submit completed projects on time with all required artifacts: specifications, reference code, tests, and peer review feedback